{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10ec88358>]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L08krLHE6jvGAA7ln+M+KTG7q05aEmCZOx6kWKwzGeBER4aGRyRw9XcTMpbud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\nY85hhcEYY8w5rDAYY4w5x/8Hju4yNasYAikAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105577438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "x = np.linspace(0,2*np.pi, 100)\n",
    "y = np.sin(x)\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
